A platform for detecting, reporting, removing, analyzing, and consulting on content, brand, and NFT infringement on the web

Abstract

The present invention provides an advanced platform for detecting, analyzing, and mitigating counterfeit products, digital content, brands, and non-fungible tokens (NFTs) online. Leveraging artificial intelligence, machine learning, computer vision, and natural language processing, the system automates the identification of unauthorized assets through visual and metadata analysis. A sophisticated web scraping subsystem gathers data from online platforms, overcoming anti-scraping barriers with asynchronous operations and IP rotation. The platform automates enforcement by generating DMCA takedown requests, de-indexing infringing content, and issuing legal notices, with insights displayed on a performance dashboard. A machine learning module, enhanced by adversarial AI simulations, adapts to evolving counterfeit methods. User interactions are facilitated by a local language model and retrieval-augmented generation system, offering tailored responses. Integrated IP consulting modules provide strategic guidance, supported by in-house attorneys. Designed for industries like e-commerce, fashion, and manufacturing, this platform safeguards digital and physical assets in the online ecosystem.

Description

A platform for detecting, reporting, removing, analyzing, and consulting on content, brand, and NFT infringement on the web

[0001] invention relates to a platform that helps content owners protect their creative assets from counterfeiting and infringement in the digital world. The platform targets brands, digital content companies, and NFT creators who face the risk of losing revenue and reputation due to consumers buying fake goods and content online. The platform provides a solution to this problem by using machine learning models to automate the detection and enforcement of IP and NFT violations in online marketplaces. The platform also uses neural networks, Natural Language Processing, and computer vision to assign rarity points to NFTs and to identify and remove unauthorized NFTs. The platform aims to reduce the sales of fake goods and content online and to increase consumer confidence and brand reputation. The platform also offers real-time market analysis for IP infringement to help content owners understand the size and scope of the problem and to take effective action against offenders. The platform enables content owners to focus on high-value strategic activities rather than engaging in manual detection of fraud and violations. The platform is a dedicated tool for IP and NFT protection in the digital world.

[0002] G06F 16 / 00 - Information retrieval; Database structures therefor; File system structures therefor [2019.01]

[0003] G06N 3 / 0475 - Generative networks [2023.01]

[0004] G06N 3 / 094 - Adversarial learning [2023.01]

[0005] G06N 5 / 00 - Computing arrangements using knowledge-based models

[0006] G06N 20 / 00 - Machine learning [2019.01]

[0007] G06F 21 / 10 - Protecting distributed programs or content, e.g. vending or licensing of copyrighted material (protection in video systems or pay television H04N 7 / 16) [2013.01]

[0008] G06F 40 / 00 - Handling natural language data (speech analysis or synthesis, speech recognition G10L) [2020.01]

[0009] G06Q 20 / 06 - Private payment circuits, e.g. involving electronic currency used only among participants of a common payment scheme [2012.01]

[0010] G06Q 50 / 18 - Legal services; Handling legal documents [2012.01]

[0011] G10L

[0012] US20190354744

[0013] Detection of counterfeit items based on machine learning and analysis of visual and textual data

[0014] A system detecting counterfeit items based on machine learning and analysis of visual and textual data is disclosed. The system may comprise a data access interface to receive product data associated with a protected product from a user device. The product data may comprise multimodal data that describes the protected product. The system may also comprise a search term generator to generate search terms based on the received product data. The system may comprise a processor to identify one or more potential counterfeit items from the at least one web source using a crawling technique to obtain data associated with to similar products from the at least one web source, identifying at least one match for similar products, and using image processing and analysis to determine if the at least one match for similar products comprises at least one or more potential counterfeit items. The system may also generate a takedown notice to the at least one web source if a user confirms the one or more potential counterfeit is items.

[0015] The patent US20190354744 introduces a machine learning-based system for detecting counterfeit items through multimodal analysis of visual and textual data, focusing on web scraping and image processing. While effective for identifying counterfeit items on online marketplaces, its scope is narrower than the present disclosure, which extends to protecting diverse IP assets, including physical goods and blockchain-based digital items such as NFTs. Unlike US20190354744, the present disclosure leverages advanced AI techniques, such as auto fine-tuning and locally hosted large language models (LLMs), to enhance the power of multi-modal retrieval augmented generation (RAG) and counterfeit detection. This approach allows the present disclosure to dynamically adapt to evolving counterfeit strategies by improving multimodal data analysis and integrating self-learning mechanisms for superior detection accuracy. Furthermore, the present disclosure incorporates robust enforcement mechanisms, including automated DMCA takedown notices, legal notifications, and blockchain-based authenticity verification. Additionally, the present disclosure employs web scraping techniques in combination with IP rotation to bypass blocking mechanisms on websites, ensuring uninterrupted data collection for enhanced counterfeit detection. Its utilization of locally hosted LLMs ensures data privacy and efficiency, making it a versatile and advanced solution compared to US20190354744.

[0016] US20140172495

[0017] System and Method for Automated Brand Protection

[0018] Brand threat information is identified relating to potential threats to one or more brands of one or more organizations. A characteristic of one or more operating environments is identified and a relation is determined between a particular one of the potential threats and the characteristic. The determined relation is used to determine risk associated with a particular brand of an organization.

[0019] The present disclosure diverges significantly from the teachings of US20140172495 by addressing advanced counterfeit identification and intellectual property enforcement mechanisms rather than general brand protection. While the cited invention pertains to the determination of risk by identifying relationships between brand threats and operational environment characteristics, the present disclosure introduces a system employing multi-modal retrieval-augmented generation (RAG), computer vision, and adversarial artificial intelligence (AI) to detect and mitigate counterfeit products, digital assets, and non-fungible tokens (NFTs). This disclosure incorporates sophisticated web scraping capabilities, utilizing adaptive prompts and IP rotation to ensure comprehensive data acquisition alongside automated enforcement modules for the submission of DMCA notices, takedown requests, and content de-indexing. Unlike the cited invention, which relies on static risk assessments, the present disclosure leverages self-learning algorithms and adversarial AI to adapt dynamically to emerging counterfeiting methodologies. Furthermore, this disclosure addresses the protection of NFTs and digital assets through blockchain verification for authenticity, supported by an interactive performance dashboard that facilitates real-time analytics, visualization, and user engagement via an integrated chatbot. The disclosed invention further enhances intellectual property enforcement by providing global automated enforcement mechanisms and strategic advisory features, establishing a comprehensive framework that significantly advances the scope and application of the cited art.

[0020] US20240007502

[0021] Automated Social Media-Related Brand Protection

[0022] A method for defending against malicious profiles on the web comprises the steps of: i) inspecting a profile to determine its relevance to a brand that it is desired to protect from malicious activity; ii) determining whether the said profile is relevant to said brand; iii) if it is determined that said profile is relevant, analyzing it to determine whether it is legitimate or malicious; and iv) if it is determined that the profile is malicious, assembling proof of its malicious activity and submitting same together with a takedown request to the administrator of the website where the profile was located.

[0023] The present disclosure differs fundamentally from the teachings of US20240007502 by addressing a broader scope of intellectual property protection that extends beyond social media profiles to encompass counterfeit goods, NFTs, and digital assets across e-commerce platforms and NFT marketplaces. While the cited invention focuses on identifying malicious profiles using parameter-based threshold comparisons, the present disclosure employs advanced technologies such as multi-modal RAG, blockchain verification, and adversarial AI to detect counterfeiting and unauthorized content. Additionally, the present disclosure features fully automated enforcement mechanisms, including DMCA notices, takedown requests, and de-indexing actions, which surpass the manual submission processes outlined in the cited invention. Unlike the static threshold-based approach of US20240007502, the present disclosure leverages self-learning algorithms and dynamic adversarial simulations to adapt to emerging threats. Furthermore, the present disclosure integrates an interactive performance dashboard with real-time analytics and user engagement through a chatbot, offering a level of interactivity absent in the cited invention. These features collectively establish a comprehensive framework for intellectual property enforcement and brand protection that is distinct from and more expansive than the scope of US20240007502.

[0024] EP3186944

[0025] Systems And Methods For Handling Fraudulent Uses of Brands

[0026] The disclosed computer-implemented method for handling fraudulent uses of brands may include (1) enabling a subscriber of a brand-protection service to select an action to perform when fraudulent use of a brand is detected in Internet traffic that is transmitted via any of a plurality of Internet-traffic chokepoints that are managed by the brand-protection service, (2) monitoring, at each of the plurality of Internet-traffic chokepoints, Internet traffic for fraudulent uses of brands, (3) detecting, while monitoring the Internet traffic, the fraudulent use of the brand, and (4) performing the action in response to detecting the fraudulent use of the brand. Various other methods, systems, and computer-readable media are also disclosed.

[0027] The present disclosure is distinguishable from EP3186944 by addressing a broader and more sophisticated scope of intellectual property protection, encompassing counterfeit goods, NFTs, and unauthorized digital assets in addition to brand protection. Unlike the cited invention, which focuses on Internet traffic monitoring at chokepoints to detect fraudulent brand usage, the present disclosure targets platforms and marketplaces, employing advanced technologies such as computer vision, multi-modal retrieval-augmented generation (RAG), and blockchain verification for detecting and authenticating digital content. Furthermore, the present disclosure automates enforcement actions, including DMCA takedowns, de-indexing, and legal notices, while integrating an interactive performance dashboard with real-time analytics and user interaction features absent in EP3186944. By leveraging dynamic self-learning algorithms and adversarial AI, the present disclosure adapts to emerging counterfeiting strategies, surpassing the static, subscriber-defined detection and action mechanisms of the cited art. These features establish a comprehensive framework for marketplace and digital asset protection, significantly advancing beyond the scope of EP3186944.

[0028] US20210279743

[0029] System and Method For Brand Protection Based on Search Results

[0030] A method of and a system for reducing access to a web resource are provided. The method comprises: receiving an information indicative of a brand to be protected; identifying a set of most popular search queries associated with the brand; acquiring a set of search results for at least one of the set of most popular search queries; calculating a harmfulness coefficient for at least one website contained in the set of search results; identifying the at least one website having the harmfulness coefficient exceeding a threshold value and defining it as a fraudulent website; generating an investment damage score for the fraudulent website; and causing execution of a brand protection measure against the fraudulent website in accordance with the investment damage score.

[0031] The present disclosure is distinct from US20210279743 in its broader and more advanced approach to intellectual property protection, addressing counterfeit goods, NFTs, and digital assets alongside fraudulent websites. Unlike the cited invention, which focuses on analyzing search result data to detect harmful websites based on harmfulness coefficients and investment damage scores, the present disclosure employs advanced technologies such as computer vision, machine learning, and blockchain verification for detecting counterfeit products and unauthorized digital assets. The present disclosure also automates enforcement actions, including DMCA takedowns, content de-indexing, and legal notices, while integrating an interactive dashboard with real-time analytics and user engagement features absent in US20210279743. Additionally, the present disclosure targets a wider array of platforms and marketplaces, using multi-modal retrieval and adversarial AI to dynamically adapt to emerging counterfeiting techniques, surpassing the static statistical analysis of the cited invention. These innovations establish a comprehensive and scalable framework for digital marketplace protection, far exceeding the scope of US20210279743.

[0032] EP2984577A1

[0033] Device, System, and Method of Protecting Brand Names and Domain Names

[0034] A computerized method of protecting a brand name of a brand owner, includes: (a) crawling a global communication network to identify and collect data about web-sites that possibly abuse the brand name; (b) for each web-site that possibly abuses the brand name, analyzing whether or not the web-site abuses the brand name by analyzing at least one of: (i) content of the web-site; and (ii) data about an owner of the web-site. The method further includes: for each web-site that possibly abuses the brand name, (A) generating an investment score indicating an estimated level of investment that was invested in development of the web-site; and (B) generating a damage score indicating a level of damage that the web-site is estimated to produce to the brand name.

[0035] The present disclosure is distinct from EP2984577 in its broader and more advanced approach to intellectual property protection, addressing counterfeit goods, NFTs, and digital assets alongside abusive websites. While the cited invention emphasizes scoring mechanisms (investment, damage, relevance, and popularity) and pattern detection to evaluate websites abusing brand names, the present disclosure employs cutting-edge technologies such as machine learning, adversarial AI, and blockchain verification to dynamically detect and protect intellectual property. Additionally, the present disclosure automates enforcement actions like DMCA takedowns, content de-indexing, and global legal notices, integrating a performance dashboard with real-time analytics and user interaction capabilities absent in EP2984577. By targeting diverse platforms, using multi-modal analysis, and leveraging blockchain verification, the present disclosure establishes a comprehensive framework that surpasses the static and domain-specific scope of EP2984577.

[0036] CN109741217

[0037] An Intellectual Property Protection System And Method Based On a BlockChain

[0038] The invention discloses a blockchain-based intellectual property protection system, which comprises a distributed storage platform, an intellectual property platform, a blockchain platform and a client, and is characterized in that an author submits a work file to the intellectual property platform through the client; the distributed storage platform realizes distributed storage of the work filesfrom the intellectual property platform, distributes a hash value for each file as an intellectual property fingerprint according to the contents of the work files, and returns the intellectual property fingerprint; after the intellectual property platform obtains intellectual property fingerprints, on one hand, non-repeated work files form intellectual property data, and the intellectual propertydata are submitted to the block chain platform; on the other hand, intellectual property data responded by the endorsement of the blockchain platform is submitted to the blockchain platform; and the blockchain platform writes the endorsed intellectual property data into the block and links the endorsed intellectual property data to the blockchain. The intellectual property protection of the work can be effectively realized.

[0039] The present disclosure is distinct from CN109741217 in its advanced approach to intellectual property protection, encompassing counterfeit detection, marketplace enforcement, and NFT protection alongside blockchain-based verification. While the cited invention focuses on storing, certifying, and authenticating intellectual property using blockchain technology, the present disclosure integrates cutting-edge technologies such as computer vision, adversarial AI, and multi-modal retrieval-augmented generation (RAG) to detect and mitigate counterfeit products and unauthorized digital assets. Unlike CN109741217, which is limited to certification and traceability, the present disclosure automates enforcement actions, including DMCA takedowns, content de-indexing, and legal notices, while providing interactive features like a performance dashboard and real-time analytics. Additionally, the present disclosure targets counterfeit NFTs and employs blockchain for verifying authenticity and ownership, extending beyond the static certification framework of the cited patent. These innovations establish a proactive and comprehensive system for intellectual property enforcement, far exceeding the scope of CN109741217.

[0040] CN115730279

[0041] NFT Work Storage and Copyright Protection Method Based on Block Chain, IPFS, and Digital Watermarking Technology

[0042] The invention discloses an NFT work storage and copyright protection method based on a blockchain, an IPFS, and a digital watermark technology, and the method specifically comprises the steps: a user submits NFT data through a client module, then a data processing module adds a digital watermark to the NFT data, the IPFS stores the NFT data after the digital watermark is added to obtain a file hash, and the file hash is stored in the client module; and finally, the Hash of the IPFS is stored on the block chain through the NFT smart contract to create the NFT. The IPFS is used as a main storage mode of the data, so that the problems generated by a traditional HTTP network protocol are solved, and the defects of long response time and high storage cost caused by overhigh storage pressure of a block chain platform due to direct storage of the NFT original data on the block chain are further avoided. The application of the intelligent contract technology and the digital watermarking technology ensures the non-modifiability and traceability of the Hash value corresponding to the NFT, and provides technical support for solving the copyright dispute problem of the NFT.

[0043] The present disclosure is distinct from CN115730279 in its broader and more dynamic approach to intellectual property protection, encompassing counterfeit detection, marketplace enforcement, and NFT protection alongside blockchain verification. While the cited invention focuses on securing NFT ownership and copyright using IPFS and digital watermarking, the present disclosure integrates advanced technologies such as computer vision, adversarial AI, and multi-modal retrieval-augmented generation (RAG) to detect and mitigate counterfeit products and NFTs across digital marketplaces. Additionally, the present disclosure automates enforcement actions like DMCA takedowns, content de-indexing, and legal notices, providing real-time monitoring and analytics through an interactive performance dashboard. By addressing counterfeit detection and marketplace enforcement, the present disclosure significantly expands the scope and functionality of NFT and digital asset protection, surpassing the storage and verification focus of CN115730279.

[0044] CN115099976

[0045] Intellectual Property Intelligent Transaction Method and System With Information Security Protection

[0046] The invention provides an intellectual property intelligent transaction method and system with information security protection. In order to overcome the defects in the existing NFT application technology, the invention provides the intellectual property intelligent transaction method and system with information safety protection, which have the advantages of better experience effect, customer information safety, intelligent contract transaction, intellectual property product safety, product transaction traceability, transaction account safety and easy maintenance. The method comprises the following steps: checking products on which the intellectual property is put on a shelf, starting to display the intellectual property without problems, starting transaction by using an upgraded intelligent contract, collecting, sorting, screening, filtering, integrating, packaging and packaging transaction data, and enabling the whole transaction process to be completely open and transparent in an upper chain and traceable in the whole process. After the transaction is finished, the data is analyzed, summarized and concluded and cannot be tampered, the system regularly performs global infringement retrieval on the intellectual property products, early warning is performed at the first time when infringement is found, and evidences are collected to prepare right protection and counterfeiting prevention.

[0047] The present disclosure is distinct from CN115099976 in its broader and more dynamic approach to intellectual property protection, extending beyond secure IP transactions to include counterfeit detection, NFT protection, and automated enforcement. While the cited invention emphasizes blockchain-based secure transactions, enhanced smart contracts, and infringement searches, the present disclosure integrates cutting-edge technologies such as computer vision, adversarial AI, and multi-modal RAG to dynamically detect and mitigate counterfeit goods and unauthorized digital assets. Additionally, the present disclosure automates enforcement actions, including DMCA takedowns and de-indexing, providing real-time monitoring and analytics through an interactive performance dashboard. By addressing counterfeit detection and enforcement across digital marketplaces and NFTs, the present disclosure surpasses the transactional and security-focused scope of CN115099976, offering a comprehensive solution for IP protection and counterfeit mitigation.

[0048] CN108881244

[0049] Block Chain - Based Internet Essay Intellectual Property Protection Method

[0050] The invention belongs to the field of intellectual property protection, and provides a blockchain-based Internet essay intellectual property protection method for solving the technical problems thatin existing Internet essay intellectual property protection, whether or not the work is infringed is difficult to detect, and quantization on the plagiarism degree is in deficiency. The block chain-based Internet essay intellectual property protection method comprises the following steps of intellectual property rights stating in the first stage; reprinting authorization in the second stage; and rights protection in the third stage. According to the method, the granularity of Internet essay intellectual property protection can be set, an original author of an Internet essay can state the Internet essay intellectual property in a fine granularity mode, after it is discovered that the Internet essay is infringed, illegal reprinted content is subjected to Hash processing by a server and thencompared with a Hash value array of the original article to obtain a reprinting proportion value, and then the plagiarism degree is quantized; and accordingly, the original author can more conveniently protect the rights of the original author according to the infringing degree of an infringer, and the protection effort on the network essay intellectual property is increased.

[0051] The present disclosure distinguishes itself from CN108881244 by addressing a wider range of intellectual property protection needs, including counterfeit detection, NFT safeguarding, and automated enforcement. While the cited invention focuses on blockchain-based plagiarism detection and IP protection for internet essays, the present disclosure leverages advanced technologies such as computer vision, adversarial AI, and multi-modal RAG to dynamically detect and mitigate counterfeit goods and digital assets. Additionally, the present disclosure automates enforcement actions like DMCA takedowns, content de-indexing, and legal notices, integrating real-time monitoring and analytics through an interactive performance dashboard. By providing comprehensive IP protection across multiple asset types and platforms, the present disclosure far exceeds the essay-focused and reactive capabilities of CN108881244, offering a proactive and scalable solution for modern IP challenges.

[0052] IN202341042489

[0053] Harnessing AI and Data Mining for a robust E-commerce Fraud Detection Model

[0054] The proposed invention presents a computer-implemented method for e-commerce fraud detection by leveraging data mining, machine learning, and artificial intelligence techniques. Transactional data from an e-commerce platform is analyzed in real-time, identifying patterns indicative of fraudulent activities. Machine learning algorithms classify transactions as legitimate or suspicious, continuously updating a fraud detection model. Real-time alerts are generated for potentially fraudulent transactions, enabling timely intervention. The model adapts to evolving fraud patterns through artificial intelligence algorithms, incorporating historical data for improved accuracy. Advanced anomaly detection techniques and integration with existing fraud prevention systems enhance overall fraud prevention capabilities. The invention offers statistical analysis and reports on detected fraudulent activities, facilitating further investigation and prevention measures.

[0055] The present disclosure is distinct from IN / 2023 / 41042489 in its comprehensive approach to intellectual property protection, addressing counterfeit detection in addition to fraud prevention. While the cited invention emphasizes real-time fraud detection through AI-driven anomaly detection and data mining, the present disclosure integrates advanced technologies like computer vision, adversarial AI, and multi-modal RAG to detect and mitigate counterfeit goods and unauthorized digital assets. Moreover, the present disclosure incorporates blockchain technology for NFT authentication, offering a level of transparency absent in IN / 2023 / 41042489. Additionally, the present disclosure automates enforcement actions such as DMCA takedowns, content de-indexing, and legal notices, providing proactive protection against intellectual property violations. With its interactive performance dashboard, real-time analytics, and marketplace-specific counterfeit detection, the present disclosure surpasses the fraud-centric capabilities of IN / 2023 / 41042489, offering a scalable and adaptable solution for protecting intellectual property in diverse digital ecosystems.

[0056] US20220232029

[0057] Systems and methods for machine learning-based digital content clustering, digital content threat detection, and digital content threat remediation in machine learning-based digital threat mitigation platform

[0058] A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse / digital fraud and not digital abuse / digital fraud; and identifying members or content clusters having digital fraud / digital abuse based on querying the distinct clusters of content data.

[0059] The present disclosure differentiates itself from US20220232029 by providing a broader and more advanced approach to intellectual property protection, counterfeit detection, and enforcement. While the cited invention focuses on spam and digital fraud detection using machine learning-based clustering and threat remediation workflows, the present disclosure leverages cutting-edge technologies such as computer vision, adversarial AI, and multi-modal RAG to detect counterfeit goods and unauthorized digital assets. Additionally, the present disclosure integrates blockchain for NFT authentication and traceability, offering a level of transparency and security absent in US20220232029. Moreover, the present disclosure automates enforcement actions like DMCA takedowns and content de-indexing, complemented by an interactive performance dashboard for real-time analytics and user engagement. By addressing a wider range of threats and employing comprehensive detection and enforcement mechanisms, the present disclosure surpasses the content-centric capabilities of US20220232029, offering a scalable and versatile solution for protecting intellectual property across diverse platforms and industries.

[0060] US20230316282

[0061] Systems And Methods For Generating a Probationary Automated-Decisioning Workflow In a Machine Learning-Task Orient Digital Threat or Digital Abuse Mitigation System

[0062] A machine learning-based method for accelerating a generation of automated fraud or abuse detection workflows in a digital threat mitigation platform includes identifying a plurality of distinct digital event features indicative of digital fraud; automatically deriving a plurality of distinct digital event decisioning criteria based on the plurality of distinct digital event features and a digital event data corpus associated with a target subscriber; automatically constructing a probationary automated fraud or abuse detection workflow based on the plurality of distinct digital event decisioning criteria, and deploying the probationary automated fraud or abuse detection workflow to a target digital fraud prevention environment associated with the target subscriber.

[0063] The present disclosure significantly diverges from US20230316282 by addressing a broader range of intellectual property (IP) and counterfeit-related threats. While US20230316282 automates the generation of fraud detection workflows for digital events using machine learning, the present disclosure employs advanced AI techniques, including adversarial AI and computer vision, to detect counterfeit goods, unauthorized digital assets, and fraudulent transactions. Moreover, the present disclosure integrates blockchain technology to authenticate NFTs and provide immutable records for IP enforcement, offering capabilities that are absent in US20230316282. By encompassing enforcement mechanisms like DMCA takedowns, de-indexing counterfeit listings, and legal notifications, the present disclosure provides comprehensive protection for brands and IP, beyond the scope of workflow-centric fraud mitigation in US20230316282. Its real-time adaptability, multi-modal analysis, and diverse applications establish the present disclosure as a versatile and robust solution for counterfeit detection and IP protection.

[0064] The present invention introduces an advanced platform designed to address the pervasive issue of counterfeit products, digital content, brands, and non-fungible tokens (NFTs) in online environments. By leveraging cutting-edge technologies such as artificial intelligence (AI), machine learning, computer vision, and natural language processing, the platform delivers a scalable, automated solution to combat intellectual property (IP) violations effectively.

[0065] The platform features an advanced web scraping subsystem that excels in gathering and processing data from a wide range of online platforms and NFT marketplaces. This subsystem is equipped with adaptive prompts and dynamic IP rotation, enabling it to operate undetected and without interruption. Its asynchronous operations ensure rapid and conflict-free data extraction across multiple marketplaces, while sophisticated anti-scraping measures bypass challenges such as rate limits, CAPTCHA, and geographic restrictions. The scraping engine captures a comprehensive dataset, including structured and unstructured information such as product listings, metadata, pricing details, user reviews, visual content, and seller information. Its dynamic adaptability ensures consistent functionality even as marketplace layouts change.

[0066] AI-powered detection and analysis form the core of the platform’s capability to identify counterfeit and unauthorized assets. By combining computer vision and machine learning techniques, the system accurately detects fraudulent listings through the analysis of visual patterns, metadata inconsistencies, and semantic indicators. This ensures reliable detection even as counterfeit methods evolve over time.

[0067] The platform includes an automated reporting and enforcement mechanism to handle infringements swiftly. Through bot-driven modules, it generates and submits DMCA notices, takedown requests, and legal letters with minimal human intervention. It also incorporates a de-indexing subsystem to remove infringing content from search engine results and legal action modules to enhance its enforcement capabilities, ensuring comprehensive protection against IP violations.

[0068] To stay ahead of emerging threats, the platform integrates dynamic learning and countermeasure development. Self-learning modules continuously refine detection algorithms and thresholds, while adversarial AI simulations model potential counterfeit strategies. This ensures the platform evolves alongside evolving counterfeit tactics, maintaining its effectiveness over time.

[0069] Strategic insights and consulting support are offered through an intuitive dashboard that consolidates key data on counterfeit trends, seller risks, and infringement hotspots. This system, combined with the expertise of in-house IP specialists, provides actionable recommendations for optimizing brand protection and enforcement strategies, helping stakeholders navigate the complexities of online marketplaces.

[0070] An interactive user engagement feature enhances the platform’s usability. A locally hosted language model, paired with retrieval-augmented generation (RAG) technology, allows users to interact with the system seamlessly. The platform integrates text and image embeddings to deliver context-aware responses, empowering users with precise, data-driven insights to inform their decision-making.

[0071] By integrating these capabilities, this invention establishes a new standard for combating counterfeiting and safeguarding intellectual property. It is particularly valuable for stakeholders in industries such as e-commerce, entertainment, fashion, and manufacturing, where the protection of brand reputation and IP is critical. This platform empowers users with the tools to ensure the security and authenticity of their digital and physical assets in an increasingly complex online ecosystem.

[0072] The exponential growth of e-commerce platforms, social media, and digital marketplaces has intensified the exposure and susceptibility of brands, digital content, and non-fungible tokens (NFTs) to counterfeiting, plagiarism, and fraudulent activities. These illicit practices undermine the financial stability and reputation of intellectual property (IP) owners while simultaneously threatening consumer trust, safety, and satisfaction. The issue is further aggravated by the increasingly sophisticated and evolving nature of online infringement techniques, which exploit the dynamic characteristics of the digital ecosystem.

[0073] Existing approaches to IP and NFT protection are inadequate and inefficient, primarily because they rely on manual or semi-automated processes. These methods are not only resource-intensive, costly, and time-consuming, but also prone to human error, rendering them ineffective at addressing the scale and complexity of the problem. Furthermore, these traditional methods lack adaptability to the fast-changing online environment, where new and more sophisticated forms of infringement, such as metadata manipulation, counterfeit product listings, and unauthorized duplication of digital assets, emerge constantly.

[0074] The fragmented and diverse nature of online platforms adds another layer of complexity, as IP owners face difficulties in addressing infringements across multiple marketplaces with varying policies, formats, and enforcement mechanisms. Existing solutions are ill-equipped to provide a unified and comprehensive approach for detecting, reporting, and removing counterfeit products and unauthorized digital assets in real time. They also fail to offer scalable and automated capabilities to address infringements effectively across vast datasets and diverse marketplaces.

[0075] There is, therefore, a pressing need for a novel, advanced, and fully automated solution that leverages cutting-edge technologies to overcome these limitations. Such a solution must integrate robust web scraping capabilities to collect data comprehensively across diverse platforms, AI-powered detection systems to identify counterfeits with high accuracy, and automated enforcement mechanisms to ensure swift and effective action against infringements. Additionally, it must be adaptive to the dynamic nature of the online ecosystem, evolving to counteract emerging threats while providing stakeholders with actionable insights and strategic recommendations for brand protection.

[0076] This need underscores the demand for a system that not only mitigates the risks of counterfeiting and IP abuse but also empowers IP owners with scalable, reliable, and precise tools to secure their assets and uphold consumer trust in a complex and ever-changing digital landscape.Solution of problem

[0077] This invention addresses the challenges of counterfeit detection, intellectual property protection, and authenticity verification in the rapidly evolving online ecosystem by introducing an advanced, comprehensive platform that leverages artificial intelligence, machine learning, and multi-modal processing. The platform is designed to provide an integrated suite of tools for detecting counterfeits, scraping data, verifying authenticity, and managing enforcement actions in NFT marketplaces and other digital environments. By automating complex processes and incorporating adaptive technologies, this solution mitigates the limitations of existing systems, enabling efficient, scalable, and accurate responses to counterfeiting threats.

[0078] The platform is underpinned by an initialization and setup subsystem that establishes the operational environment for asynchronous data scraping and processing. Key functionalities, such as the asynchronous operation handling module, ensure seamless execution of concurrent tasks across multiple marketplaces without conflicts, while the configuration loading module optimizes settings for large language models (LLMs), embeddings, and IP rotation. These foundational elements create a robust framework for efficient and adaptive data management.

[0079] A sophisticated data scraping subsystem collects data from diverse NFT marketplaces, using adaptive prompts and IP rotation to bypass anti-scraping defenses. This subsystem ensures comprehensive coverage by leveraging targeted prompts and dynamically managing source definitions across multiple platforms. Advanced execution mechanisms enable scalable data extraction tailored to capture detailed metadata, descriptions, and other critical information.

[0080] The platform incorporates an IP protection and middleware subsystem to maintain undetected access to marketplaces. Components like the IP rotation middleware module and request management module ensure uninterrupted operations by rotating IP addresses and pacing requests effectively, avoiding detection and blocking mechanisms.

[0081] A multi-modal retrieval-augmented generation (RAG) and local LLM configuration subsystem enhances the platform’s capability to analyze text, images, and metadata. By employing locally hosted language models and embedding configurations, the system processes diverse data types with precision, enabling accurate counterfeit detection. Automated fine-tuning modules further adapt the model to evolving counterfeit patterns, ensuring its relevance and effectiveness over time.

[0082] To organize and interpret the collected data, the result processing and level 1 reporting subsystem generates structured reports highlighting counterfeit likelihood and associated metadata. Advanced formatting and storage modules ensure the data is accessible and actionable, while the integration of adversarial AI simulations and counterfeit scoring mechanisms refines the detection process.

[0083] Image processing and computer vision modules analyze visual data, comparing product images against a database of authentic items to identify discrepancies indicative of counterfeit characteristics. Feature extraction and semantic analysis provide further insights, integrating visual and textual cues to enhance detection accuracy. This multi-modal approach ensures robust identification of counterfeit listings and fraudulent assets.

[0084] The platform automates enforcement actions through a bot module capable of generating and submitting DMCA notices, takedown requests, and legal letters. This module also identifies high-risk sellers using data-driven insights and facilitates investigations by local enforcement teams. The legal and seller intelligence modules enable proactive measures to combat counterfeiting.

[0085] Performance tracking is centralized in a dashboard module that consolidates key metrics, including de-indexing statuses, takedown outcomes, and legal actions. Advanced analytics and visualization features provide stakeholders with actionable insights into counterfeiting trends and the effectiveness of anti-piracy efforts. Notifications and alerts ensure timely responses to high-probability counterfeit detections.

[0086] Finally, the platform features a responsive multi-modal RAG module that processes user queries with precision, integrating text and image retrieval mechanisms to deliver context-aware responses. A chatbot interface facilitates seamless user interactions, enabling stakeholders to access the platform’s capabilities intuitively.

[0087] This invention thus provides a unified, adaptive, and automated solution that comprehensively addresses the technical challenges of counterfeit detection and intellectual property protection, offering stakeholders in e-commerce, entertainment, fashion, and other industries the tools necessary to safeguard their assets and maintain consumer trust in a dynamic digital environment.Advantage effects of invention

[0088] The present invention achieves significant and transformative advantages in the detection, reporting, and mitigation of counterfeit goods and intellectual property violations, particularly in the realms of non-fungible tokens (NFTs) and other digital assets. By employing a sophisticated integration of artificial intelligence, machine learning, and multi-modal processing, the invention ensures a robust, automated, and scalable approach to intellectual property protection, overcoming the inefficiencies and limitations of existing methodologies.

[0089] The modular architecture of the invention enables seamless scalability, facilitating the processing of vast datasets from diverse and dynamic sources, including NFT marketplaces, social media platforms, and online commerce websites. Through the use of an advanced web scraping subsystem with features such as dynamic IP rotation, adaptive prompts, and asynchronous task handling, the invention ensures uninterrupted and undetected data collection. This comprehensive data acquisition capability provides stakeholders with actionable insights, leaving no critical information overlooked.

[0090] One of the key innovative effects of the invention lies in its ability to integrate visual and semantic analysis for counterfeit detection. The computer vision module performs high-precision analysis of visual attributes—such as textures, shapes, logos, and packaging designs—while the semantic analysis subsystem identifies textual anomalies and inconsistencies in product descriptions, reviews, and seller metadata. By combining these multi-modal insights, the invention achieves superior detection accuracy, even against sophisticated and adaptive counterfeiting techniques.

[0091] The automation of enforcement processes further exemplifies the invention’s advantageous effects. By automating the generation and submission of Digital Millennium Copyright Act (DMCA) notices, takedown requests, and de-indexing actions, the system ensures immediate and effective responses to infringements. The invention’s legal enforcement subsystems, including the generation of legal notices and consultations with in-house intellectual property experts, provide a seamless interface between automated enforcement and human-led legal strategies. This ensures compliance with regional and international legal standards while maintaining operational speed and accuracy.

[0092] Another critical effect of the invention is its self-learning capability, which incorporates adversarial AI simulations to preemptively counteract emerging counterfeiting strategies. By dynamically updating detection thresholds and refining its models with real-world and simulated data, the system ensures resilience and adaptability over time. This continuous evolution enables the system to address both current and future counterfeiting threats effectively.

[0093] The invention’s seller intelligence and risk management features introduce a proactive dimension to counterfeit mitigation. By analyzing seller behaviors and tracing counterfeiting networks across platforms, the system enables targeted and prioritized enforcement actions. This targeted approach amplifies the impact of anti-counterfeiting measures, conserving resources while maximizing results.

[0094] The performance dashboard provides a centralized, interactive platform for monitoring and managing anti-counterfeiting activities. By consolidating enforcement metrics, visualizing real-time trends, and issuing critical alerts, the dashboard empowers stakeholders to make informed and timely decisions. Integrated tools, such as the chat bot and tailored recommendations, further enhance accessibility and user engagement, ensuring that the system is both functional and intuitive for a wide range of users.

[0095] In summary, the invention presents a revolutionary advancement in intellectual property protection and counterfeit mitigation. Its novel integration of advanced technologies, automation, and self-adaptive mechanisms establishes an unparalleled standard for safeguarding both digital and physical assets. By addressing immediate enforcement needs and long-term strategic goals, the invention delivers comprehensive, efficient, and future-ready solutions to a pressing global challenge.

[0096] : NFT Marketplace Overview: Illustrates data sources and modules involved in gathering NFT listings, metadata, and other assets from various marketplaces.

[0097] : Initialization and Setup Subsystem: Shows the configuration for enabling asynchronous operations, handling events, and loading essential configurations for data scraping.

[0098] : Data Scraping Subsystem: Outlines the scraping process with modules for prompt configuration, source management, and scraper execution to extract data from marketplaces

[0099] : Outlines the IP Protection and Middleware Subsystem, a critical component designed to ensure secure, undetectable web scraping. By employing advanced IP rotation and request management strategies, this subsystem prevents blocking, detection, or banning during interactions with NFT marketplaces.

[0100] : illustrates the Multi-Modal RAG (Retrieval-Augmented Generation) and Local LLM Configuration Subsystem, a sophisticated system designed to enhance processing capabilities by leveraging text, image, and metadata handling via RAG techniques paired with a locally hosted LLM. The subsystem focuses on natural language understanding, embedding creation, and interaction with downstream systems.

[0101] : details the Result Processing and Level 1 Reporting Subsystem, a critical stage where data from prior processes, including scraping and analysis, is organized, formatted, and transformed into actionable outputs. This subsystem focuses on generating structured reports, flagging anomalies, and ensuring that the results are ready for further downstream processing and review.: Machine Learning Module: Demonstrates adversarial simulations, counterfeit scoring, and continuous learning for identifying counterfeit products.

[0102] : presents the Machine Learning Module, an advanced system for classifying products, particularly NFTs, as genuine or counterfeit. It leverages pre-trained models, adversarial AI simulations, and semantic analysis to improve detection accuracy. The module integrates visual and non-visual data, applies self-learning principles, and generates counterfeit likelihood scores for decision-making.

[0103] : describes the Computer Vision Module, a vital component designed to extract and analyze visual features from product images (e.g., NFTs) to detect counterfeit items. It integrates with a Semantic Analysis Module for enhanced textual data evaluation, combining both visual and textual analyses to improve classification accuracy. The module leverages feature extraction, visual comparison, and a reference database of genuine products.

[0104] : depicts the Bot and Legal Enforcement Module, a comprehensive framework designed to handle the identification, reporting, and takedown of counterfeit content and sellers, particularly fraudulent NFTs. This subsystem integrates a legal and operational approach to ensure efficient enforcement of intellectual property rights while providing actionable insights for stakeholders.

[0105] : represents the Performance Dashboard and Analytics Subsystem, a critical module designed to track, display, and analyze the outcomes of anti-counterfeiting efforts. It integrates data from various sources to provide consolidated insights, performance metrics, and actionable advice while supporting user interactions through queries and visualized results.

[0106] : depicts the Responsive Multi-Modal Retrieval-Augmented Generation (RAG) Module, an advanced subsystem designed for handling text and image queries. It integrates embedding, retrieval, and re-ranking techniques to generate highly relevant, multi-modal responses. This module is central to user interaction workflows, enhancing query response accuracy and relevance by leveraging a local large language model (LLM) with embedding capabilities.

[0107] : Marketplace Sources: The figure lists different NFT marketplaces (NFT Marketplace 1, NFT Marketplace 2, NFT Marketplace 3, etc.), each represented as a unique data source. These marketplaces provide diverse data inputs, allowing the system to capture a wide array of NFT listings, metadata, and multimedia content.

[0108] URLs and Source Paths: For each marketplace, specific URLs (e.g., URL1, URL2, URL3) are designated, enabling the system to access various content endpoints. These paths may correspond to specific pages or sections within each marketplace, ensuring comprehensive data capture across categories, types, and formats.

[0109] Database Integration: Collected data includes multiple formats-videos, documents, photos, and other digital assets related to NFTs. This setup highlights the system's adaptability in processing structured and unstructured data, allowing for detailed analyses.

[0110] Data Scraping Subsystem / Source Definition Module: The data collected from these sources is then passed to the Data Scraping Subsystem, specifically the Source Definition Module. This module defines and manages the configuration of each data source, ensuring that scraping operations align with the marketplace's structure and rules. This module enables targeted and effective data collection by organizing URLs and setting parameters.

[0111] :illustrates the Initialization and Setup Subsystem, which serves as the foundational entry point of the platform. This subsystem establishes an environment conducive to efficient asynchronous operations and seamless data handling. Its architecture is designed to prepare the system for critical tasks such as data scraping and processing by managing configuration loading and ensuring integration across all operational components.

[0112] The Asynchronous Operation Handling Module within this subsystem plays a crucial role in facilitating the smooth execution of concurrent tasks. By employing components like nest_asyncio, the module resolves event loop conflicts that are often encountered in web scraping. This ensures a stable environment, even when multiple asynchronous tasks are executed simultaneously. The subsystem’s architecture is specifically designed to minimize processing delays and conflicts, providing robust support for real-time scraping and integration. Successfully handled asynchronous requests are passed downstream to other subsystems, such as the Data Scraping Subsystem, ensuring efficient task flow throughout the platform.

[0113] The Configuration Loading Module initializes critical system configurations for operational components, including Large Language Models (LLMs), embeddings, and IP rotation mechanisms. For LLMs, the module ensures that the necessary parameters and settings are preloaded, enabling subsequent analysis to run smoothly. For embeddings, it prepares the system for semantic data representation and optimization, crucial for tasks like search and data comparison. Additionally, IP rotation mechanisms are initialized to manage rotating IP addresses, preventing IP bans during web scraping. By preloading these configurations, the module establishes a cohesive and effective data processing pipeline, reducing setup time and enhancing runtime efficiency. This module also interacts directly with external libraries and services to ensure all dependencies are correctly initialized before data scraping begins.

[0114] The relationships and interactions within the Initialization and Setup Subsystem are central to its functionality. This subsystem directly feeds into the Data Scraping Subsystem, ensuring the operational environment is fully configured and stable before intensive data extraction begins. Each module within the Initialization Subsystem plays a critical role in creating a strong foundation for subsequent processes, reducing the risk of errors in downstream operations. For example, the Asynchronous Operation Handling Module ensures that the system can manage a large volume of requests concurrently without interference. If the Data Scraping Subsystem receives high workloads, the asynchronous handling mechanism queues and processes these requests without crashing the system.

[0115] Configuration dependencies managed by the Configuration Loading Module are vital to the system’s efficiency. This module sets parameters for data scraping, such as access credentials, rotating IPs for scraping anonymity, and pre-trained embeddings for semantic understanding. These dependencies ensure that data scraping tasks are not only accurate but also protected from common issues like IP blocking or request throttling by the target platforms.

[0116] The subsystem is also designed for high performance and reliability. By integrating asynchronous operations, it can handle large workloads while maintaining system stability. Preloading configurations minimizes the setup time required during runtime, enabling faster initialization of tasks like scraping and processing. Modular components, such as nest_asyncio, ensure scalability, allowing the system to handle an increasing number of asynchronous tasks and configurations as the platform grows. Additionally, the subsystem prioritizes error prevention by initializing critical features like IP rotation and asynchronous handling early in the process. This proactive approach avoids common issues such as IP blocking and task conflicts during data scraping, ensuring a stable and predictable environment for all subsequent operations.

[0117] :represents the Data Scraping Subsystem, a critical component responsible for collecting targeted data from multiple NFT marketplaces. The subsystem emphasizes adaptability and flexibility in its scraping processes, employing robust IP protection mechanisms to avoid detection and blockage. Its design ensures comprehensive data acquisition from diverse sources, supporting subsequent analysis and processing.

[0118] The Scraping Prompt Configuration Module is a key element of this subsystem, providing the functionality to define and configure specific prompts for data collection. These prompts dictate the type and scope of data to be retrieved, such as NFT listings, metadata, descriptions, and images. For example, prompts like “List all NFTs with metadata and description” are used to guide the system’s focus during scraping operations. This module is highly adaptable, allowing for different data collection scenarios tailored to the dynamic and ever-changing nature of online marketplaces. The configured prompts are outputted to the Scraper Execution Module, where they are executed as part of the data scraping workflow.

[0119] The Source Definition Module manages a dynamic catalog of source URLs for various NFT marketplaces. It ensures comprehensive coverage by organizing and maintaining a list of target sources, enabling thorough data acquisition. This module is designed to support continuous updates and expansions of source lists without disrupting the system’s operations. By serving as a foundational element, it feeds directly into the execution phase, ensuring that data is collected from diverse and relevant marketplaces.

[0120] The Scraper Execution Module functions as the operational core of the subsystem, executing web scraping processes using a mechanism like SmartScraperGraph.run(). It combines configurations and prompts to carry out detailed and efficient scraping operations. The module adheres to IP rotation protocols and integrates middleware for enhanced security, ensuring safe and undetectable operations. This module manages the workflows associated with data collection and acts as a hub for executing and overseeing scraping tasks.

[0121] The interactions within the Data Scraping Subsystem are central to its integration with the broader platform. Incoming interactions from the Initialization and Setup Subsystem provide necessary configurations and ensure readiness for scraping tasks, such as asynchronous operation support and IP rotation. The subsystem also integrates with the IP Protection and Middleware Integration system, which enables undetectable and uninterrupted data scraping. Furthermore, prompts or parameters informed by the Multi-Modal RAG and Local LLM Configuration Subsystem refine the scraping targets, enhancing the accuracy and relevance of data collection.

[0122] The data collected by the subsystem is transmitted to subsequent processing modules for storage, machine learning analysis, or other downstream applications. The subsystem serves as a bridge between raw data sources (NFT marketplaces) and the processing layers that utilize the collected data. This workflow includes preparation through initialization, adaptive execution guided by prompts and configurations, and secure operations supported by IP rotation and middleware mechanisms.

[0123] The subsystem’s modular design ensures scalability, allowing the easy addition of new marketplaces or updates to scraping prompts. Its integration with asynchronous handling systems enables the efficient execution of multiple simultaneous scraping tasks. The high configurability of scraping prompts ensures adaptability to diverse data collection requirements, while middleware and IP rotation mechanisms guarantee stable and reliable operation, even under stringent anti-scraping measures imposed by marketplaces.

[0124] The embodiments and highlights of the Data Scraping Subsystem demonstrate its interoperability with upstream and downstream systems, customizability for real-time adjustments, and robust security measures. Its seamless integration with other systems supports efficient workflows, while its advanced IP rotation and middleware mechanisms maintain its undetectable status, ensuring reliable, long-term operation in a variety of online environments.

[0125] :illustrates the IP Protection and Middleware Subsystem, which is designed to anonymize and safeguard the scraping activities carried out by the platform. By rotating IP addresses for each request, this subsystem ensures that scraping operations remain undetected by anti-scraping mechanisms employed by NFT marketplaces. The subsystem is critical to maintaining reliable and continuous functionality, even under strict scrutiny.

[0126] The IP Rotation Middleware Module is a central component of this subsystem. Its primary function is to dynamically update IP addresses for every request, making each interaction appear as though it originates from a new user. This middleware employs advanced IP management techniques to bypass detection while maintaining the continuity of operations. By acting as a security layer, it integrates seamlessly with upstream modules such as the Initialization Subsystem for configuration and the Scraping Subsystem for execution. The middleware is designed to operate in real-time, ensuring that all scraping processes are protected against marketplace anti-scraping measures.

[0127] The Request Management Module ensures that requests are timed, paced, and retried in a manner that avoids triggering anti-scraping mechanisms. This module employs algorithms to dynamically monitor and adjust the frequency of requests, simulating human-like browsing behavior. By balancing scraping speed with security, it prevents system overloads that could lead to detection. Additionally, the module integrates retry mechanisms to recover gracefully from failed requests, ensuring data acquisition processes are robust and efficient.

[0128] The subsystem also coordinates closely with the Data Scraping Subsystem through its IP Protection and Middleware Integration functionality. This integration ensures that IP rotation is consistently active throughout scraping operations. Acting as an intermediary, the subsystem pre-emptively addresses potential detection threats by rerouting requests or rotating IPs dynamically. This fail-safe mechanism ensures uninterrupted functionality and alignment with the overall scraping workflow.

[0129] The interactions within the IP Protection and Middleware Subsystem are carefully structured. Incoming interactions from the Initialization and Setup Subsystem provide configuration for IP rotation, preloading the middleware and ensuring it is operational before scraping begins. Additionally, the subsystem receives operational parameters, such as timing thresholds, IP pools, and retry strategies, from configuration modules. Outgoing interactions with the Data Scraping Subsystem facilitate secure and undetectable operations by dynamically updating IP addresses for each request and passing controlled, paced requests to the Scraping Execution Module for data acquisition.

[0130] The IP Protection and Middleware Subsystem plays a central role in the system’s workflow. During the preparation phase, it receives initial configurations from the Initialization Subsystem to establish IP rotation middleware. During execution, the subsystem operates in real time to manage IPs and requests securely. Its protective functionalities continuously evaluate the operational environment for detection risks, dynamically adjusting operations to ensure seamless data scraping.

[0131] In terms of performance and reliability, the subsystem is highly scalable, supporting a wide range of scraping tasks across multiple marketplaces by utilizing extensive IP pools and adaptive request management strategies. It balances request timing with operational speed, enabling fast yet undetectable scraping. The system’s retry mechanisms further enhance efficiency by recovering from failed requests without disrupting the workflow. Middleware integration ensures robust and uninterrupted protection against detection or blocking, while continuous monitoring and adjustments minimize the risk of anti-scraping defenses identifying scraping activities. This design ensures that the subsystem provides a secure and reliable foundation for all data scraping operations.

[0132] :illustrates the Multi-Modal RAG and Local LLM Configuration Subsystem, which enables advanced data processing through the integration of retrieval-augmented generation (RAG) techniques and a locally hosted Large Language Model (LLM). This subsystem processes diverse data formats, including text, images, and metadata, to provide a comprehensive understanding of the collected information. Its design ensures efficient and autonomous operation while maintaining data privacy and security.

[0133] The Multi-Modal RAG Module is a key component of the subsystem, handling multi-modal inputs for tasks such as detection, similarity analysis, and contextual understanding. By combining text, images, and metadata, this module employs RAG techniques to generate outputs informed by both real-time queries and stored knowledge. It serves as a bridge between raw scraped data from the Data Scraping Subsystem and downstream analytical processes, dynamically configuring queries for tasks like similarity detection or detailed content analysis.

[0134] The Local LLM Configuration Module manages a locally hosted LLM, such as ollama / llama3, to execute robust natural language processing tasks. This module offers fine-grained control over model parameters, including temperature control for output randomness and JSON output formatting to ensure compatibility with downstream systems. By operating independently of external APIs, the module ensures system privacy, reduces latency, and generates tailored outputs for specific queries and data analysis needs.

[0135] The Embedding Configuration Module configures embedding models, such as ollama / nomic-embed-text, to convert text and image data into embeddings for similarity detection and efficient data retrieval. By encoding information into vectorized formats, this module enables advanced analytical tasks, such as comparing NFTs based on textual descriptions and visual attributes. It integrates seamlessly with other subsystems, supporting a range of downstream processing tasks.

[0136] The Server Communication Module facilitates interactions between local servers, the LLM, and embedding models. This module ensures smooth execution of requests and responses, acting as the backbone for internal communication within the subsystem. By maintaining autonomy from external dependencies, it supports the efficient operation of local models and preserves data security.

[0137] The Automate Fine-Tuning Module provides the capability to further customize and train the LLM or embedding models using custom datasets. This module allows the subsystem to adapt to evolving requirements by incorporating new data into model training, enhancing accuracy and performance for specific use cases.

[0138] Incoming interactions for the subsystem originate from the Data Scraping Subsystem, which supplies raw data, including text, images, and metadata, for processing and analysis. Additionally, the Initialization and Setup Subsystem provides the necessary configurations and initialization parameters for the LLM and embedding models. Outgoing interactions include the transmission of processed data to the database for storage, such as embeddings and JSON-structured outputs, and the delivery of insights to the Result Processing and Level 1 Reporting Subsystem for further analysis and reporting.

[0139] The Multi-Modal RAG and Local LLM Configuration Subsystem plays a central role in the platform’s workflow. It begins by receiving multi-modal inputs from the Data Scraping Subsystem. These inputs are processed using RAG techniques and embedding models for similarity detection and knowledge extraction, producing structured outputs suitable for storage or reporting. The subsystem also supports fine-tuning, enabling it to adapt to specific requirements for improved accuracy and performance.

[0140] In terms of performance and reliability, the subsystem is highly scalable, supporting a variety of data types and use cases. Its localized operation ensures scalability without reliance on external APIs. Efficiency is achieved through the creation of embeddings and JSON formatting, which streamline data flow and retrieval processes, while local hosting reduces latency and enables near-real-time processing. The subsystem’s autonomy from external servers enhances its reliability and security, and robust error-handling mechanisms ensure consistent operation across diverse data formats. This design positions the subsystem as a cornerstone of advanced data understanding and transformation within the platform.

[0141] :illustrates the Result Processing and Level 1 Reporting Subsystem, which is tasked with processing, organizing, and storing results derived from data scraping and multi-modal analysis. This subsystem prepares data for further use in modules such as computer vision or semantic analysis while also generating insights and flagging anomalies. Its functionality bridges the gap between raw data and actionable outputs, ensuring that results are accessible and useful for downstream systems.

[0142] The Result Storage Module serves as the core repository for the results produced by the system’s upstream processes, such as SmartScraperGraph.run(). It organizes and archives data including flagged or suspicious NFTs, similarity scores for detecting duplicates or counterfeit assets, and metadata related to analyzed entities. Acting as a structured repository, the module interfaces directly with upstream systems, such as the Multi-Modal RAG subsystem. It employs tagging and indexing techniques to optimize searchability and categorization, ensuring that results can be retrieved efficiently for further analysis or reporting.

[0143] The JSON Formatting and Prettifying Module plays a crucial intermediary role by converting raw outputs into a structured, readable JSON format. This transformation ensures that the data is consistently stored and compatible with downstream systems. By applying standardized formatting rules, this module minimizes the risk of errors in data interpretation or usage, enhancing the reliability and accessibility of processed outputs. Its design simplifies the integration of complex analyses into subsequent workflows.

[0144] The Reporting and Flagging Mechanism generates actionable reports and provides insights derived from processed data. It identifies and flags counterfeit NFTs based on similarity scores, metadata inconsistencies, and other analytical criteria. The mechanism also creates detailed descriptions of anomalies and generates visualizations or summaries to enhance interpretability for human users. By bridging raw data and decision-making tools, this component offers both technical insights for advanced users and high-level summaries for strategic evaluation.

[0145] Incoming interactions for the subsystem primarily originate from the Multi-Modal RAG and Local LLM Configuration Subsystem, which supplies processed data such as embeddings, similarity scores, and flagged entities. Outgoing interactions include transmitting structured JSON data to the Semantic Analysis Module for further evaluation and providing flagged NFTs, product images, and related metadata to the Computer Vision Module for advanced authenticity checks. These interactions position the subsystem as a central hub for data routing and preparation.

[0146] The workflow of the Result Processing and Level 1 Reporting Subsystem involves several key stages. First, it aggregates outputs from scraping and multi-modal processing activities during the result collection phase. Next, the subsystem structures this data into a JSON format, facilitating integration with downstream systems and ensuring readability. Finally, it generates structured reports and supplies critical insights to modules responsible for advanced analysis or decision-making. Built-in error-handling mechanisms ensure that flagged data is stored correctly and any inconsistencies are documented.

[0147] In terms of performance and reliability, the subsystem is designed for scalability, enabling it to manage a high volume of results from multiple upstream processes. Its efficiency is enhanced by the use of JSON formatting and structured storage, which minimize overhead and allow for real-time reporting. Robust data handling mechanisms ensure the reliability of report generation, reducing the risk of lost or misrepresented information. This subsystem provides a critical foundation for delivering accurate, actionable insights within the broader system workflow.

[0148] :illustrates the Machine Learning Module, which serves as a critical component for classifying NFTs and related products as genuine or counterfeit. This subsystem employs comprehensive data analysis and adaptive learning to ensure accurate and robust counterfeit detection. Its design enables continuous refinement of detection mechanisms through self-learning and adversarial simulations, ensuring adaptability to evolving threats.

[0149] The Machine Learning Module leverages pre-trained models as its core knowledge base. These models are trained on historical patterns of counterfeit products, allowing the module to identify discrepancies based on both visual and non-visual features. By integrating these features into its decision-making process, the module delivers robust initial classifications. Additionally, these pre-trained models are continuously updated with new data received from the Self-Learning Module, maintaining their relevance and effectiveness over time.

[0150] The Adversarial AI Simulation Module strengthens the system’s defenses against counterfeit threats by simulating potential future counterfeiting techniques. By introducing these simulations into the detection pipeline, the system anticipates evolving methods of fraud and proactively adjusts its detection mechanisms. This module acts as a proactive defense layer, enhancing the robustness of counterfeit identification and feeding simulated data into the learning pipeline for resilience improvements.

[0151] The module also integrates semantic analysis to complement visual feature evaluations. By analyzing textual data, such as product descriptions, seller information, and customer feedback, the system can detect anomalies and inconsistencies indicative of counterfeit activity. This integration of non-visual insights provides a more holistic assessment, enabling the module to identify sophisticated patterns of fraud that might not be evident through visual analysis alone.

[0152] The Counterfeit Scoring Module assigns a counterfeit likelihood score to each item based on metadata discrepancies, visual and textual similarity thresholds, and other evaluative criteria. This score provides a quantitative metric for assessing authenticity, acting as the final step in the classification process. The scoring system supports decision-making by offering a transparent and objective measure of counterfeit likelihood, which is particularly useful for downstream actions like de-indexing or reporting.

[0153] The Self-Learning Module ensures that the system evolves dynamically with emerging trends in counterfeiting. This module continuously refines detection models by incorporating new data and feedback from both simulations and false classifications. By learning from errors and updating its algorithms, the module minimizes classification inaccuracies over time, thereby enhancing the system’s reliability and adaptability.

[0154] Incoming interactions for the Machine Learning Module include structured data and visual feature vectors from the Computer Vision Module, textual data and identified anomalies from the Semantic Analysis Module, and structured metadata from the Result Processing and Level 1 Reporting Subsystem. These inputs collectively feed into the classification pipeline, enabling the module to aggregate and analyze diverse data types for comprehensive assessments.

[0155] Outgoing interactions include transmitting identified counterfeit products to the De-Indexing Module for removal or blacklisting. Additionally, new insights and classifications are sent to the Automate Fine-Tuning Module, which uses this information to improve model accuracy and performance through iterative fine-tuning processes.

[0156] The workflow of the Machine Learning Module begins with aggregating structured and unstructured data from upstream systems, such as computer vision and semantic analysis subsystems. Pre-trained models, adversarial simulations, and scoring mechanisms are then applied to classify items based on their likelihood of being counterfeit. The subsystem outputs actionable decisions, including counterfeit likelihood scores, for use in downstream processes.

[0157] In terms of performance and reliability, the Machine Learning Module is highly scalable, capable of processing large datasets and handling inputs from diverse sources simultaneously. Its design ensures adaptability to new counterfeiting trends through adversarial simulations and learning updates. Efficiency is achieved by leveraging pre-trained models for rapid initial assessments, while semantic analysis and adversarial simulations enable deeper, context-aware evaluations. The module’s reliability is further enhanced by robust self-learning mechanisms, which minimize classification errors over time, and the transparent counterfeit scoring system, which provides a clear basis for decision-making. This subsystem is a cornerstone of the platform’s ability to detect and mitigate counterfeiting risks effectively.

[0158] :depicts the Computer Vision Module, a subsystem designed to extract and analyze the visual attributes of product images to identify patterns indicative of counterfeit or genuine products. This module serves as the primary visual analysis engine within the system, comparing extracted features with known data to detect discrepancies. By integrating advanced visual and contextual text analysis, it ensures a comprehensive evaluation of products for authenticity.

[0159] The Feature Extraction component focuses on analyzing product images to identify critical attributes such as color, shape, texture, logos, and specific characteristics like packaging design. These attributes are used to detect visual patterns or anomalies that distinguish counterfeit items from genuine ones. The extracted features are converted into structured data, represented as feature vectors, to enable efficient comparison and classification in subsequent processes.

[0160] The Visual Comparison component compares the extracted feature vectors against a database of known genuine products. By analyzing visual similarities and discrepancies, this component determines the authenticity of the product. It acts as the decision-making step within the module, generating results that inform downstream classification tasks, such as counterfeit likelihood scoring.

[0161] The Library Module maintains a repository of images and textual data associated with known counterfeit patterns. This library is continuously updated with new information, ensuring that the system remains adaptive to emerging trends and techniques in counterfeiting. It serves as a dynamic resource for both feature extraction and visual comparison, enriching the module’s analytical capabilities with contextual references.

[0162] The module also incorporates a Semantic Analysis Module, which processes unstructured textual data such as product reviews, social media mentions, and seller descriptions. By detecting anomalies in textual data, this module flags potential counterfeit items or untrustworthy sellers. It enhances the overall classification process by providing contextual insights from associated text, operating as a complementary module to the visual analysis components.

[0163] Incoming interactions for the Computer Vision Module originate from the database, which supplies reference images of genuine products for visual comparison. The module also receives confirmed counterfeit or genuine product data from the Result Processing and Level 1 Reporting Subsystem to refine detection criteria. Outgoing interactions include transmitting extracted features and visual analysis results to the Machine Learning Module for classification and counterfeit scoring. Additionally, analyzed results, such as flagged products and suspicious sellers, are sent to the Result Processing and Level 1 Reporting Subsystem for structured reporting and decision-making.

[0164] The workflow of the Computer Vision Module begins with the extraction of distinct visual attributes from product images, capturing features that distinguish genuine products from counterfeit ones. These features are then compared against a database of known genuine products to identify anomalies or inconsistencies. Simultaneously, the module evaluates contextual textual data, such as unusual descriptions or seller behavior, to flag suspicious activities. The visual and textual insights are integrated into a comprehensive result for classification or reporting.

[0165] In terms of performance and reliability, the module is highly scalable, capable of handling large datasets comprising multiple images and associated textual data. Its library module ensures adaptability to new counterfeiting patterns and trends, while streamlined feature extraction and comparison processes enable fast and accurate visual analysis. The integration with semantic analysis further enhances decision-making efficiency by leveraging diverse data types. Reliability is bolstered by continuous updates to the library module and database, ensuring robustness against evolving counterfeiting tactics. Collaboration with downstream modules, such as the Machine Learning Module, ensures ongoing refinement and accuracy, making the Computer Vision Module a cornerstone of the system’s counterfeit detection framework.

[0166] :illustrates the Bot Module, a subsystem designed to automate and streamline enforcement processes, including the generation and submission of takedown requests, de-indexing of infringing content, and legal enforcement. This subsystem leverages advanced automation and expert-driven strategies to provide a comprehensive approach to intellectual property protection and counterfeit mitigation.

[0167] The Bot Module incorporates a DMCA Notice Generation and Submission Component, which drafts and submits Digital Millennium Copyright Act (DMCA) notices automatically. Using predefined templates and data from confirmed counterfeit listings, this component functions as a real-time enforcement mechanism, targeting websites hosting infringing content. Similarly, the Takedown Module automates the generation and submission of takedown requests directed at specific URLs or hosts, ensuring the prompt removal of counterfeit materials. These processes work in tandem to enhance the system’s reach and effectiveness in eliminating infringing content.

[0168] The De-Indexing Module removes infringing content from search engine indexes in real time. By integrating seamlessly with takedown processes, this module ensures comprehensive removal of counterfeit materials from public access, thereby reducing their visibility and impact. Together, these components create an automated enforcement pipeline capable of handling routine tasks efficiently.

[0169] The subsystem also includes Legal Enforcement Components that support more complex and targeted actions. The Legal Notice Module generates and dispatches legal notices to infringers, incorporating regional expertise and involving local teams for investigations and follow-ups when necessary. This expands the scope of enforcement efforts beyond automated processes. Additionally, the IP Consulting Module engages in-house intellectual property attorneys to review infringing listings, provide strategic advice on takedown processes, and offer insights into IP protection. These modules act as a bridge between automated enforcement and expert-driven strategies, ensuring alignment with legal and regulatory frameworks.

[0170] A complementary Intellectual Property Strategy Module offers proactive recommendations for safeguarding legitimate content. This module provides data-driven strategies to optimize legitimate content listings, minimizing the risk of counterfeiting. By focusing on long-term content security, it adds a strategic dimension to the enforcement efforts.

[0171] The Seller Intelligence Module collects and analyzes data on sellers to identify high-risk individuals or groups and trace the sources of counterfeiting activities. By matching data across platforms, this module enables targeted enforcement actions, prioritizing efforts on high-risk sellers and counterfeit networks. This targeted approach enhances the efficiency and impact of enforcement processes.

[0172] For monitoring and reporting, the subsystem incorporates a Counterfeit Report Generation Module and a Data / Performance Dashboard Module. The report generation component compiles detailed summaries of flagged NFTs, counterfeit scores, and associated metadata, offering actionable insights to stakeholders. Meanwhile, the dashboard provides a centralized interface displaying real-time metrics, such as high-risk sellers and takedown performance, consolidating outputs from all modules to support informed decision-making.

[0173] Incoming interactions for the Bot Module include data on verified counterfeit items from upstream processes, which feed into the enforcement mechanisms. Insights from the Seller Intelligence Module guide targeted actions against high-priority counterfeit sources. Outgoing interactions provide data and recommendations to legal and operational teams, as well as metrics and flagged content to the performance dashboards for comprehensive tracking.

[0174] The workflow of the Bot Module integrates both automated and expert-led strategies. Automated systems handle routine takedown tasks and de-indexing, ensuring real-time enforcement. Legal and consulting modules complement these efforts by aligning enforcement actions with IP laws and regional regulations. The subsystem combines proactive measures, such as optimizing legitimate content listings and tracking high-risk sellers, with reactive takedown efforts, creating a balanced approach to counterfeit mitigation.

[0175] The Bot Module is designed for scalability, capable of handling large volumes of flagged content while maintaining efficiency through automation. It is adaptable to global operations, integrating with local teams for enforcement actions across regions. Real-time mechanisms ensure swift removal of counterfeit content, while comprehensive reporting and dashboards enhance decision-making processes. Reliability is further strengthened by the involvement of legal and consulting modules, which provide a robust foundation for enforcement and minimize risks of oversight or errors. This subsystem serves as a critical component of the platform’s overarching strategy to protect intellectual property and combat counterfeiting effectively.

[0176] :illustrates the Performance Dashboard Module, which functions as the central interface for visualizing consolidated data, metrics, and actionable insights related to anti-counterfeiting activities. This module tracks progress, results, and status updates, offering decision-makers an intuitive overview of the system’s performance and the effectiveness of enforcement actions.

[0177] The Consolidated Infringement Data Display serves as the primary interface for displaying comprehensive data related to high-risk sellers, sources of counterfeiting activities, and involved marketplaces and assets. By integrating tracking for both physical and digital assets, this component provides a unified view that enables decision-makers to identify trends and prioritize enforcement actions effectively.

[0178] The Visualization Module transforms raw data into interactive graphical formats such as charts, maps, tables, and dashboards. By presenting data in visually intuitive formats, this module enhances user comprehension and supports better decision-making. It serves as a bridge between complex datasets and actionable insights, ensuring clarity and accessibility for stakeholders.

[0179] The Performance Analytics Module evaluates the magnitude of losses caused by counterfeiting, assesses the impact of anti-piracy efforts, and calculates key performance metrics (KPIs). It also analyzes the effectiveness of user reports in identifying counterfeit products. This component provides real-time analytics, enabling the system to identify areas of improvement and ensure continuous enhancement of anti-counterfeiting efforts.

[0180] The Alert and Notification Module acts as a proactive mechanism by sending alerts for high-probability counterfeit detections, prompting immediate investigation and action. This ensures that potential threats are addressed promptly, minimizing the impact of counterfeit activities and improving the system’s responsiveness.

[0181] The subsystem also incorporates Advisory and Consulting Components to provide strategic recommendations. The Data-Driven Advice Module offers guidance on protecting legitimate content, optimizing its representation, and refining removal strategies. By delivering actionable insights through statistical and trend analysis, this module aids stakeholders in making informed decisions. The User Interaction Module, which includes an interactive chat bot, enables users to query the system, upload image or text data, and receive tailored responses. Integrating with the Multi-Modal RAG Module, the chat bot provides advanced natural language processing capabilities, serving as an accessible tool for stakeholders to obtain specific information or request reports.

[0182] Incoming interactions for the Performance Dashboard Module originate from multiple upstream sources. The Counterfeit Report Generation Module provides flagged products, counterfeit scores, and associated metadata for visualization and reporting. Takedown and Legal Notice Modules supply the status of takedown requests, legal actions, and de-indexing progress. Data from the IP Consulting and Seller Intelligence Modules delivers insights into high-risk sellers, counterfeiting sources, and recommended actions. The Intellectual Property Strategy Module contributes suggestions for content protection and optimization, enriching the data displayed on the dashboard.

[0183] Outgoing interactions include sharing user queries and input data with the Multi-Modal RAG Module for advanced response generation. Additionally, consolidated insights and performance metrics are provided to decision-makers, supporting strategic planning and policy formulation.

[0184] The workflow of the Performance Dashboard Module involves aggregating data from multiple upstream modules and visualizing it in user-friendly formats for interpretation. Real-time performance monitoring tracks the progress and effectiveness of anti-counterfeiting efforts, offering stakeholders continuous insights into ongoing operations. The module also allows users to interact with the system, view results, and receive tailored recommendations. Proactive alerts ensure timely action on critical issues, further enhancing the system’s efficiency and reliability.

[0185] In terms of performance and reliability, the Performance Dashboard Module is scalable to handle data from large-scale operations, ensuring adaptability to diverse use cases and growing datasets. Its integration of visualization, analytics, and interactive features provides a robust foundation for monitoring and decision-making, making it a vital tool in the system’s overall anti-counterfeiting framework.

[0186] :illustrates the Responsive Multi-Modal Retrieval-Augmented Generation (RAG) Module, which serves as an advanced subsystem for handling user queries through the integration of text and image processing. This module is designed to deliver accurate, context-aware responses by leveraging embedding models, vector stores, and re-ranking mechanisms to optimize retrieval and response generation.

[0187] The module incorporates Embedding Modules that transform user inputs, including text and images, into vector embeddings for semantic similarity searches. Text embeddings encode textual data into numerical representations for efficient matching, while image embeddings convert visual features into numerical formats for image-based retrieval. These embeddings form the foundational processing layer for multi-modal data, enabling the system to perform high-speed retrieval and analysis with precision.

[0188] The Vector Stores act as repositories for indexed embeddings, supporting efficient and rapid data retrieval. These stores are divided into text vector stores for managing textual data embeddings and image vector stores for housing embeddings of visual data. Upon receiving query embeddings, the system retrieves the top-N most relevant chunks, such as text passages or image references, to ensure high-quality matching and analysis. This architecture supports quick lookups and efficient processing, even for large-scale datasets.

[0189] The Multi-Modal Re-Ranker refines the retrieved results by prioritizing the most relevant chunks based on their relevance to the user’s query. By considering both textual and visual contexts, this component ensures that the system delivers responses that are not only accurate but also contextually appropriate. This re-ranking process optimizes the response accuracy, making the system more effective in addressing complex queries.

[0190] The Multi-Modal LLM operates as the reasoning engine of the subsystem, synthesizing retrieved data from text and image embeddings to generate human-readable, contextually enriched responses. This component integrates seamlessly with the embedding and retrieval layers, allowing it to produce dynamic and tailored outputs that align with the user’s intent.

[0191] The module supports diverse inputs, including textual queries and uploaded images, allowing users to access information and insights in multiple formats. The system converts these inputs into query embeddings for both text and images, retrieves the most relevant chunks from the vector stores, and re-ranks them for optimal relevance. The final response is generated by synthesizing the retrieved data using the multi-modal LLM, blending textual and visual information for comprehensive user consumption.

[0192] Incoming interactions for the Responsive Multi-Modal RAG Module include embedding configurations from the Embedding Configuration Module, which supply the models necessary for processing text and image embeddings. Queries and uploads from the Chat Bot serve as the primary inputs for processing. Outgoing interactions include sending generated responses to the Chat Bot, combining retrieved insights with synthesized reasoning, and providing feedback to the Embedding Configuration Module to improve embedding performance over time.

[0193] The module’s workflow begins with query handling, where text and image inputs are embedded and matched against pre-stored vectors. The retrieved data chunks are then re-ranked to prioritize relevance, and the multi-modal LLM synthesizes a final response that combines insights from both textual and visual sources. This process ensures that users receive precise, actionable, and contextually relevant information.

[0194] The Responsive Multi-Modal RAG Module is designed for scalability, capable of handling large-scale vector stores and accommodating high query volumes. It supports a wide range of input types, including purely textual, purely visual, or mixed queries. Its efficiency is demonstrated through fast embedding and vector store retrieval, as well as the re-ranking and reasoning mechanisms that enhance response relevance with minimal latency. The module’s reliability is further strengthened by robust embedding and retrieval mechanisms, which ensure consistent performance across varied query types. By integrating local LLMs, the subsystem also prioritizes user privacy and reduces reliance on external APIs, making it a secure and efficient solution for advanced query handling.Examples

[0195] To illustrate the application and functionality of the invention, consider the following example of a user leveraging the platform to detect and mitigate counterfeit products in an NFT marketplace. The user, a digital artist with a popular NFT collection, encounters reports from their community that unauthorized copies of their NFTs are being sold across multiple online platforms. Concerned about both financial loss and potential damage to their reputation, the artist engages the platform to address the issue.

[0196] Upon initiating the platform, the user provides details about their original NFT collection, including metadata, images, and blockchain registration information. The system’s Initialization and Setup Subsystem configures the operational parameters required for data scraping, enabling asynchronous operations and ensuring robust IP rotation to prevent detection by anti-scraping mechanisms. The configuration includes prompts tailored to target the specified NFT marketplaces where the artist’s works are likely being counterfeited.

[0197] The platform’s Data Scraping Subsystem begins by systematically crawling the designated marketplaces and retrieving data such as NFT listings, associated metadata, seller profiles, and visual content. The adaptive scraping prompts ensure comprehensive data capture, even as marketplace layouts and anti-scraping measures evolve. This data is stored securely in the system’s database, ready for multi-modal analysis.

[0198] Once the data is collected, the Computer Vision Module analyzes the visual attributes of the retrieved NFT images. It compares the extracted features, including texture, logos, and designs, against the legitimate images provided by the artist. Simultaneously, the Semantic Analysis Module evaluates associated textual data, such as descriptions and seller information, identifying inconsistencies and anomalies indicative of counterfeit activity.

[0199] The platform’s Machine Learning Module processes the findings, integrating visual and textual insights to generate a counterfeit likelihood score for each suspicious listing. Leveraging pre-trained models and adversarial simulations, the system dynamically adjusts its detection thresholds to account for evolving counterfeiting techniques. Listings flagged with high counterfeit likelihood are then prioritized for enforcement actions.

[0200] The Bot Module automates the enforcement process by generating and submitting Digital Millennium Copyright Act (DMCA) notices to the hosting platforms. The Takedown Module supplements this by initiating requests to remove infringing listings and associated metadata from public view. The De-Indexing Module ensures that flagged content is removed from search engine results, further minimizing exposure.

[0201] To provide the user with actionable insights, the platform generates a detailed report through its Result Processing and Level 1 Reporting Subsystem. This report includes a summary of flagged listings, counterfeit likelihood scores, and metadata discrepancies, presented in a structured JSON format. The report is visualized on the platform’s Performance Dashboard, where the user can track the progress of takedown requests and monitor trends in counterfeit activities.

[0202] The system also supports proactive measures. Using the Seller Intelligence Module, the platform identifies high-risk sellers responsible for repeated infringements and traces their activities across multiple platforms. The artist is provided with a strategic overview of these counterfeiting networks, enabling targeted enforcement actions and long-term risk mitigation.

[0203] To enhance the user’s experience, the Responsive Multi-Modal RAG Module allows the artist to interact directly with the system. By querying the platform using textual or visual inputs, the artist receives tailored responses and recommendations. For example, upon uploading a newly created NFT, the system analyzes it against existing listings, ensuring no unauthorized replicas are being distributed.

[0204] Over time, the platform’s Self-Learning Module refines its detection models using feedback from completed enforcement actions, false positives, and missed detections. This continuous improvement ensures that the system remains resilient and effective against emerging counterfeiting methods.

[0205] In this example, the invention demonstrates its ability to automate and streamline complex processes, providing users with a comprehensive tool for safeguarding intellectual property. The platform not only addresses immediate counterfeiting concerns but also equips users with proactive strategies to protect their digital and physical assets, ensuring long-term content security and brand integrity.

[0206] This invention is a system for detecting, reporting, removing, cleaning, analyzing, consulting, and strategizing on counterfeit products, NFTs, and contents on the web. This invention has a high industrial applicability, because it can be used in many industries that are affected by counterfeiting and piracy, such as:

[0207] The creative industry, which includes artists, musicians, writers, filmmakers, designers, etc. who create original content and want to protect their intellectual property rights and revenues from online infringement.

[0208] The manufacturing industry, which includes producers, distributors, and retailers of physical products and goods who want to prevent counterfeit products from entering the market and damaging their brand reputation and customer trust.

[0209] The e-commerce industry, which includes online platforms and marketplaces that facilitate the buying and selling of products and services and want to ensure the quality and authenticity of their offerings and avoid legal liabilities and customer complaints.

[0210] The legal industry, which includes lawyers, attorneys, and law firms that specialize in intellectual property and anti-counterfeiting and want to provide effective and efficient solutions and services to their clients and partners.

[0211] This invention can also benefit other industries that are related to or depend on the above-mentioned industries, such as the entertainment industry, the fashion industry, the pharmaceutical industry, the education industry, the gaming industry, etc.

[0212] This invention can also create new opportunities and markets for the development and innovation of related products and services, such as:

[0213] Anti-counterfeiting software and hardware, which can help detect, report, remove, clean, analyze, consult, and strategize on counterfeit products and content on the web using advanced technologies such as artificial intelligence, machine learning, computer vision, natural language processing, blockchain, etc.

[0214] Anti-counterfeiting education and training, which can help raise awareness and knowledge about the risks and impacts of counterfeiting and piracy and the best practices and strategies to prevent and combat them among various stakeholders such as creators, consumers, sellers, platforms, authorities, etc.

[0215] Anti-counterfeiting certification and verification, which can help establish and maintain standards and criteria for the quality and authenticity of products and content on the web and provide reliable and trustworthy proofs and guarantees to the stakeholders.

[0216] Therefore, This invention has a high industrial applicability, because it can solve a significant and widespread problem that affects many industries and sectors, and it can create value and benefit for many stakeholders and customers.

Claims

A system for detecting, reporting, and mitigating counterfeit products and content across digital marketplaces, the system comprising a computer vision module configured to extract, process, and analyze visual features such as color, texture, shape, logos, and packaging designs from product images and compare extracted visual data against a dynamically updated database of verified authentic products to detect deviations indicative of counterfeiting. The system further comprises a machine learning module operable to integrate visual and non-visual data, including textual metadata, seller profiles, and customer feedback, for multidimensional counterfeit classification, and to assign a counterfeit probability score based on thresholds derived from adversarial simulations and historical patterns of detected counterfeit products. Additionally, the system includes a data scraping subsystem engineered for robust collection of marketplace data by employing asynchronous operations for efficient real-time scraping of structured and unstructured data and leveraging adaptive scraping prompts and IP rotation to circumvent anti-scraping mechanisms implemented by marketplace platforms. A bot-driven enforcement module is configured to automate the drafting and submission of DMCA notices, takedown requests, and legal notifications to infringing parties and hosting platforms, and the de-indexing of infringing content from search engine results. A performance analytics dashboard is configured to visualize consolidated infringement data, key performance metrics, and the status of enforcement actions across digital marketplaces, sellers, and products.A method for detecting, mitigating, and preventing counterfeiting in online environments, the method comprising collecting data from multiple online sources using a scraping subsystem employing asynchronous task handling, adaptive prompts, and IP rotation middleware to ensure unobstructed data collection. The method processes the collected data through a computer vision module for extracting and comparing visual features, and a machine learning module for integrating textual metadata and assigning counterfeit likelihood scores. Products are classified as genuine or counterfeit based on multimodal analysis, incorporating adversarial simulations to predict and adapt to novel counterfeiting techniques. The method automates the reporting, removal, and de-indexing of infringing content through bot-powered enforcement and generates strategic recommendations through an intellectual property consulting module to guide content protection and optimization efforts.A platform for multi-modal detection and comprehensive analysis of counterfeit products, content, and NFTs, the platform comprising a retrieval-augmented generation (RAG) module configured to process multimodal data, including text, images, and metadata, for detecting anomalies and generating context-aware outputs. The platform includes a local large language model configuration subsystem enabling secure, autonomous natural language processing through locally hosted LLMs that support configurable parameters such as temperature adjustment and JSON output formatting for downstream systems. An embedding configuration module is operable to encode text and images into vector embeddings for similarity-based retrieval and anomaly detection. A self-learning module is configured to continuously update detection algorithms and adapt to evolving counterfeiting patterns based on real-time user feedback and historical data, incorporating results from adversarial AI simulations for enhancing detection robustness.The system of claim 1, wherein the computer vision module further comprises a feature extraction subsystem configured to analyze specific product characteristics, including fine-grained patterns, edge features, and watermark detections, to ensure robust counterfeit identification, and an anomaly detection engine for identifying subtle visual irregularities that deviate from genuine product standards.The system of claim 1, wherein the machine learning module further comprises an adversarial simulation component employing generative adversarial networks (GANs) to simulate advanced counterfeiting methods and train the detection algorithms on potential future threats, and a metadata discrepancy analyzer for evaluating inconsistencies in pricing, seller reputation, and product descriptions.The data scraping subsystem of claim 1, wherein the subsystem further includes a request pacing and retry mechanism to optimize scraping throughput while adhering to anti-scraping thresholds, and an adaptive rule engine to dynamically adjust scraping prompts based on detected changes in marketplace structure or anti-scraping protocols.The bot-driven enforcement module of claim 1, further comprising a legal compliance module configured to ensure all enforcement actions, including DMCA notices and takedown requests, comply with regional intellectual property regulations, and coordinated with local legal experts for high-risk sellers and global enforcement cases.The method of claim 2, wherein the counterfeit classification further incorporates a semantic analysis module to process unstructured text, such as social media mentions, seller biographies, and customer reviews, for contextual anomaly detection, and a multi-modal ranking system to prioritize high-risk counterfeit cases for immediate enforcement.According claim 3, further comprising a predictive analytics module to forecast emerging counterfeit trends and inform proactive anti-counterfeiting measures, and a blockchain integration module to verify the authenticity and provenance of NFTs and digital assets.The system of claim 1, wherein the performance dashboard module further includes a real-time alert system to notify stakeholders of high-probability counterfeit detections, anomalies, or enforcement delays, and an interactive visualization engine providing customizable reports on enforcement outcomes and market impact metrics.The system of claim 1, wherein the embedding configuration module of the platform further supports cross-modal embeddings that integrate visual and textual data for enhanced counterfeit classification, and fine-tuning capabilities using domain-specific datasets to improve detection precision.The method of claim 2, further comprising a reporting mechanism to compile detailed infringement data, including flagged counterfeit items, associated metadata, and seller intelligence, for review by intellectual property consultants.The system of claim 1, further comprising a seller intelligence module configured to trace counterfeit networks by correlating seller activity across platforms and identifying high-risk sellers, and to provide actionable insights for targeted enforcement and long-term counterfeit mitigation strategies.The platform of claim 3, wherein the responsive multi-modal RAG module further comprises a multi-modal embedding subsystem configured to simultaneously encode text and image inputs into respective vector representations, enabling semantic alignment and cross-modal retrieval, a multi-modal re-ranking engine that prioritizes retrieved text and image chunks based on contextual relevance to user queries, optimizing response precision, and an adaptive query handling module that dynamically adjusts retrieval parameters and ranking algorithms based on historical query patterns and user preferences, and a response synthesis mechanism integrated with the locally hosted language model to generate tailored, context-aware outputs combining retrieved textual and visual data.The platform of claim 10, wherein the chatbot integrated within the performance dashboard module is further configured to process user queries involving textual, visual, or combined inputs through the multi-modal RAG module, enabling dynamic retrieval of relevant insights and performance metrics. It provides real-time updates on enforcement actions, including the status of takedown requests, DMCA submissions, and legal notices, and offers data-driven recommendations derived from the intellectual property strategy module and seller intelligence module for optimizing content protection and addressing counterfeit risks. The chatbot interacts with visualization tools within the dashboard to generate user-specific graphical summaries, reports, and alerts based on queried data.

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